Ultrasonic Image Speckle Reduction via Multiresolution Decomposition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current ultrasonic diagnostic systems face challenges in effectively and efficiently removing speckle from both two-dimensional and three-dimensional image data, particularly due to the limitations of existing methods such as multiresolution decomposition and nonlinear anisotropic diffusion filtering, which often result in images that are sensitive to artificial thresholds and require extensive processing time, especially when dealing with high-resolution data and volume data.
Innovation Solution
The proposed solution involves an ultrasonic diagnostic apparatus that performs hierarchical multiresolution decomposition of ultrasonic image data, followed by nonlinear anisotropic diffusion filtering and high-frequency level control, generating edge information to synergistically remove speckle through a combination of these processes before scan conversion, thereby enhancing image quality and processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nonlinear anisotropic diffusion filtering is applied to remove speckle, then speckle reduction effect is achieved, but processing time increases significantly
Solution Approach 1:
The patent applies multiresolution decomposition to divide the ultrasonic image into multiple frequency components (low-frequency and high-frequency parts). The nonlinear anisotropic diffusion filtering is then applied only to the low-frequency component, while the high-frequency component is preserved. This segmentation approach reduces the processing area and time while maintaining effective speckle reduction.
Solution Approach 2:
The patent performs multiresolution decomposition as a preliminary step before applying the nonlinear anisotropic diffusion filtering. By decomposing the image first, the system prepares the data in a way that allows faster and more efficient filtering, reducing the overall processing time while maintaining the speckle reduction effect.
2Device complexity
If fixed filter is used to make edge smooth and clear, then processing is simplified, but performance is limited
Solution Approach 1:
The patent uses dynamic thresholding based on the local standard deviation of pixel values to determine the filtering strength. Instead of a fixed filter, the system adapts the filtering parameters according to the image content, allowing automatic adjustment of edge smoothing and clearing performance based on the specific characteristics of each region.
Solution Approach 2:
The patent changes the filtering parameters dynamically based on the standard deviation of pixel values in different regions. By adjusting parameters such as the threshold value and filtering intensity according to local image characteristics, the system achieves better edge smoothing and clearing performance without using a complex fixed filter design.
3Measurement precision
If scan conversion processing is performed before speckle removal, then coordinate system conversion is achieved, but processing speed decreases due to high-resolution requirements
Solution Approach 1:
The patent performs multiresolution decomposition and speckle removal filtering as preliminary actions before the scan conversion processing. By completing the speckle reduction in the original coordinate system first, the system avoids the need to process high-resolution data after scan conversion, thereby maintaining processing speed while achieving accurate coordinate system conversion.
4Reliability
If multiresolution decomposition is applied to remove speckle, then speckle is removed effectively, but image depends on artificial sensibility
Solution Approach 1:
The patent uses the standard deviation of pixel values as a feedback mechanism to automatically adjust the filtering threshold and strength. This feedback approach allows the system to adapt to different image characteristics and tissue types, reducing dependence on artificial sensibility and improving the objectivity of the processing results.
Data Source
AI summary
Multiresolution decomposition of image data before scan conversion processing is hierarchically performed, low-frequency decomposed image data and high-frequency decomposed image data with first to n-th levels are acquired, nonlinear anisotropic diffusion filtering is performed on output data from a next lower layer or the low-frequency decomposed image data in a lowest layer, and filtering for generating edge information on a signal for every layer is performed from the output data from the next lower layer or the low-frequency decomposed image data in the lowest layer. In addition, on the basis of the edge information on each layer, a signal level of the high-frequency decomposed image data is controlled for every layer and multiresolution mixing of the output data of the nonlinear anisotropic diffusion filter and the output data of the high-frequency level control, which are obtained in each layer, are hierarchically performed.